Manual dexterity enables humans to manipulate small objects with our fingers, perform highly skilled tasks as well as simple activities of daily living. These actions require independent finger movements, and precise control of the direction and magnitude of fingertip forces such as those involved in grasping objects. We grasp objects of various textures, weights, and shapes by adapting our fingertip forces to friction at the grasping surface, the weight of the object, and its shape. The grip force must be optimized to prevent excessive squeezing of the object (wasted force and/or object damage) or slippage of the object from grasp (and possible breakage) due to insufficient, weak forces. The sense of touch is used perceive friction at the object surface; without tactile feedback humans tend to use too much force when grasping, or apply insufficient forces. In this study we use high-resolution 3D printing to create textured surfaces whose physical dimensions are specified and quantified in psychophysical tests. We propose to quantify and analyze manual dexterity using a grasp-and-lift task in which we measure the effects of load and surface texture on performance in healthy human adults of ages 18-80, and in subjects with central and/or peripheral neurological impairments. These experiments will provide quantitative metrics of manual dexterity and force control in young, middle-aged, and elderly subjects that can serve as baseline values for evaluating treatment and rehabilitation therapies following stroke, or peripheral nerve injury, as well as the well-known reduction in hand function as a result of aging.